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Regional flood risk caused by intensive rainfall under extreme climate conditions has increasingly attracted global attention. Mapping and evaluation of flood hazard are vital parts in flood risk assessment. This study develops an integrated framework for estimating spatial likelihood of flood hazard by coupling weighted naïve Bayes (WNB), geographic information system, and remote sensing. The north part of Fitzroy River Basin in Queensland, Australia, was selected as a case study site. The environmental indices, including extreme rainfall, evapotranspiration, net-water index, soil water retention, elevation, slope, drainage proximity, and density, were generated from spatial data representing climate, soil, vegetation, hydrology, and topography. These indices were weighted using the statistics-based entropy method. The weighted indices were input into the WNB-based model to delineate a regional flood risk map that indicates the likelihood of flood occurrence. The resultant map was validated by the maximum inundation extent extracted from moderate resolution imaging spectroradiometer (MODIS) imagery. The evaluation results, including mapping and evaluation of the distribution of flood hazard, are helpful in guiding flood inundation disaster responses for the region. The novel approach presented consists of weighted grid data, image-based sampling and validation, cell-by-cell probability inferring and spatial mapping. It is superior to an existing spatial naive Bayes (NB) method for regional flood hazard assessment. It can also be extended to other likelihood-related environmental hazard studies. [ABSTRACT FROM AUTHOR]

Flooding hazard evaluation is the basis of flooding risk assessment which has significances to natural environment, human life and social economy. This study develops a spatial framework integrating naïve Bayes (NB) and geographic information system (GIS) to assess flooding hazard at regional scale. The methodology was demonstrated in the Bowen Basin in Australia as a case study. The inputs into the framework are five indices: elevation, slope, soil water retention, drainage proximity and density. They were derived from spatial data processed in ArcGIS. NB as a simplified and efficient type of Bayesian methods was used, with the assistance of remotely sensed flood inundation extent in the sampling process, to infer flooding probability on a cell-by-cell basis over the study area. A likelihood-based flooding hazard map was output from the GIS-based framework. The results reveal elevation and slope have more significant impacts on evaluation than other input indices. Area of high likelihood of flooding hazard is mainly located in the west and the southwest where there is a high water channel density, and along the water channels in the east of the study area. High likelihood of flooding hazard covers 45 % of the total area, medium likelihood accounts for about 12 %, low and very low likelihood represents 19 and 24 %, respectively. The results provide baseline information to identify and assess flooding hazard when making adaptation strategies and implementing mitigation measures in future. The framework and methodology developed in the study offer an integrated approach in evaluation of flooding hazard with spatial distributions and indicative uncertainties. It can also be applied to other hazard assessments. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an integrated modeling method for multi-criteria land-use suitability assessment (LSA) using classification rule discovery (CRD) by ant colony optimisation (ACO) in ArcGIS. This new attempt applies artificial intelligent algorithms to intelligentise LSA by discovering suitability classification rules. The methodology is implemented as a tool called ACO–LSA. The tool can generate rules which are straightforward and comprehensible for users with high classification accuracy and simple rule list in solving CRD problems. A case study in the Macintyre Brook Catchment of southern Queensland in Australia is proposed to demonstrate the feasibility of this new modeling technique. The results have addressed the major advantages of this novel approach. [Copyright &y& Elsevier]

Fluorescent lamp manufacturing workers have been extensively exposed to mercury (Hg). Our aim was to assess their health risks using several approved occupational health risk assessment methods, and to find out which method was more suitable for identification of occupational health risks. Work locations, and air and urine samples were collected from 530 exposed workers in Zhejiang, China. Based on the calculated exposure doses, health risks and risk ratios (RRs) as health risk indices, were evaluated using: Environmental Protection Agency (EPA), Australian, Romanian, Singaporean, International Council on Mining and Metals (ICMM), and Control of Substances Hazardous to Health (COSHH) methods. Among the workers, 86.0% had higher Hg levels than the Chinese occupational exposure limits of 0.02 mg/m3, and 16.7% urine samples were higher than the biological exposure limits of 35.0 μg/g·creatinine. Among workers at the injection, etc. locations, their average RRs, evaluated by the EPA, COSHH and Singaporean methods were 0.97, 0.76, and 0.60, respectively, and were significantly higher than the ICMM (0.39), Australian (0.30) and Romanian (0.29) methods. The RRs from the Singaporean method showed significant correlations with the urinary Hg levels ( P < 0.01). In conclusion, the Singaporean method was more appropriate than the others for health risk evaluation because the excessive risks were significantly associated with urinary Hg levels among the workers., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ruan, Tan, Zhu, Shi, Yu, Zhang, Wang and Fu.)